Where one engine replaces several.

The problem

Laundering hides in patterns no single tool sees — circular flows, structuring, mule networks. Teams stitch a graph tool, an ML platform, an anomaly engine and a screening product together. Tessera runs all of it on one copy of the live transaction graph, with every alert traceable to its source facts.

Fraud cycle detection

Every circular flow (A → B → C → A) up to a chosen length — the signature of layering, circular trading and collusive rings that per-transaction rules miss. Enterprise.

Behavioural anomaly scoring

A running risk score per account, learned from that account's own normal: unusual amounts, velocity, new counterparties, amounts parked just under reporting thresholds. Enterprise.

AML typology alerts

Structuring, layering and high-velocity movement raised as a triage-ready queue with typology, severity, account and score. Enterprise Plus.

Sanctions screening

Customers, counterparties, beneficiaries and beneficial owners screened against the lists you provide, with fuzzy matching for transliterations and typos. Enterprise Plus.

Fraud-classifying GNN

A graph neural network judges an account partly by the company it keeps, trained in-database over the data already stored. Enterprise.

The problem

Vector-only retrieval matches by similarity alone — no relationships, no exact-term precision and no record of which facts an answer used, which rules it out for regulated or high-stakes AI. Tessera fuses graph, semantic and keyword signals in one query and traces every retrieved fact to its source.

Hybrid retrieval

Keyword and vector search run together and fused with Reciprocal Rank Fusion, so records strong in either or both rise to the top. Community.

Graph, vector and keyword in one query

Records semantically about a topic, connected in the graph to a given entity, and mentioning an exact term — structure, meaning and keywords reasoned over together.

Provenance on every answer

Every retrieved fact traces back to its sources and forward to what was derived from it, so you can show which facts drove an answer. Enterprise.

Vector-from-text search

Plain text in, ranked records out, embedded with the same model used on your data. Community.

Exact-term precision

BM25 matches names, codes and exact phrases alongside semantic matching, with snippets and highlighting. Community.

The problem

Customer data is scattered across systems, the same person appears many times, and the links between accounts, devices and addresses are invisible. Tessera models them as one graph and resolves duplicates with similarity and link prediction.

Unified entity graph

People, accounts, devices, addresses and interactions in one graph over one copy, queried by connection, by table or by meaning.

Duplicate resolution

Jaccard and cosine similarity over neighbourhoods find entities that connect to many of the same things — the basis for merging scattered records. Enterprise.

Hidden connections

Structural link prediction (Common Neighbours, Adamic-Adar, Katz) surfaces likely links between accounts, devices and addresses, with no training. Enterprise.

Hyperedge interactions

‘This account, on this device, at this address’ stored as one fact with named roles, not flattened into separate rows.

Three ways to query

Cypher for a traversal, SQL for a report, TQL for a temporal question — all over the same unified view.

The problem

Regulators ask what you knew and when. Reconstructing a past state across mutable, disconnected stores — and proving nobody changed the record — is painful. Tessera reconstructs any past moment, shows the record is intact, and traces every fact to its origin.

Bitemporal time-travel

‘What was true on 1 March?’ and ‘what did the database believe then?’, both answered from valid time and transaction time. Community.

Tamper-evident audit chain

An append-only, hash-chained record in which altering, deleting or reordering any entry is detected on verification. Enterprise.

Lineage and provenance

Any record traced back to its sources and forward to what it affected, with root sources and a hop-stratified provenance graph. Enterprise.

Investigation branching

Branch a past state, investigate in isolation, and keep every change out of production data. Community.

Point-in-time recovery

Replay the write-ahead log to just before a bad event, with every file checked against the manifest's checksums. Enterprise.

The problem

Enterprise knowledge sits in silos; keyword search misses meaning, and vector search misses structure and exactness. Tessera holds entities, relationships, text and embeddings in one engine, so a query reasons over all of them.

Entities, relationships and text together

One engine and one copy for entities, relationships, text and embeddings, instead of separate stores stitched together.

Hyperedge-native facts

A typed fact links any number of entities in named roles, with no invented intermediate nodes. Community.

Semantic plus keyword reasoning

Semantic similarity for meaning and BM25 for exact terms, fused with graph relationships, so answers reflect structure as well as similarity.

Schema-on-write modelling

Types register on first use and property types are inferred, so you start writing at once and evolve the schema as you learn. Community.

Three query languages

Cypher, TQL and SQL over the same knowledge graph, mixed freely across an application. Community.

The problem

Who should connect, what they should buy, which accounts will transact — questions that need graph structure and learned patterns, usually from a graph database plus a separate ML stack. Tessera runs both in-database over the live graph.

Structural link prediction

Common Neighbours, Jaccard, Adamic-Adar, Preferential Attachment and Katz give instant suggestions from graph shape, with no training. Enterprise.

Trained link-prediction models

Logistic regression, random forest or gradient boosting trained on real edges and negative samples, reported with AUC-ROC. Enterprise.

Similarity-based suggestions

Nodes scored as alike by the company they keep — ‘these two connect to many of the same things’. Enterprise.

Graph embeddings

FastRP and Node2Vec turn structural position into vectors for search, clustering or downstream models. Enterprise.

Model catalogue and evaluation

Every trained model versioned and kept through restarts, with AUC-ROC, precision, recall and F1 to compare models and sweep thresholds. Enterprise.

The problem

Supply networks are deep, many-party graphs, and ‘if this supplier fails, what breaks?’ means traversing many hops. Tessera stores many-party facts as hyperedges and uses graph algorithms to find critical nodes, routes and cascading-failure exposure.

Many-party facts as hyperedges

A step linking a product, handler, location and certifications is one hyperedge, not an invented middle node. Community.

Cascading-failure exposure

Independent Cascade and Linear Threshold diffusion show what breaks downstream when a supplier or route fails. Enterprise.

Critical-node analysis

PageRank, betweenness and closeness find the suppliers, routes and assets that hold the network together, and the bottlenecks. Enterprise.

Path and reachability mapping

Dijkstra, BFS, A* and Yen's k-shortest paths trace how exposure and value move from A to B. Enterprise.

Source-to-delivery provenance

Any product traced back to its origins and forward through every step it touched. Enterprise.

The problem

The modern AI and vector stack is cloud-native, but regulated data in banking, healthcare, government and defence often cannot leave the building. Tessera ships the hybrid and ML stack as one binary that runs entirely on your hardware, with no cloud call-home.

Single self-contained binary

One native executable — no JVM, no separate install — that starts in seconds on your own hardware. Community.

No cloud call-home

Nothing phones home: the five-dimension engine, hybrid retrieval and in-database ML all run air-gapped.

In-database ML, no export

Classification, GNN, link prediction, GAN synthetic data and online learning train and serve inside the database, so sensitive data never leaves for a separate ML system. Enterprise.

Encryption at rest

Opt-in AES-256-GCM over the audit log and storage layers, enabled without a data migration; a stolen disk or leaked backup yields only ciphertext. Enterprise.

Governance built in

RBAC on every request, OIDC and SAML single sign-on against your identity provider, the tamper-evident audit chain and fail-closed licensing. SSO and the audit chain are Enterprise.

The problem

Metrics and events usually live in a separate time-series database, cut off from the entities they describe. Tessera's native time-series is analysed against the same graph and tables the metrics belong to.

Native time-series operations

Bucketing, gap-filling, downsampling and anomaly detection over compressed storage, with no separate time-series database. Community.

Metrics beside their entities

Because every fact is bitemporal, readings are analysed against the graph and tables they belong to — trends in context, not in a disconnected metrics store.

Anomaly detection

Anomalies found in the series itself, surfacing next to the entities and relationships that produced them.

Behavioural risk over time

Per-account anomaly scoring learns each entity's normal and flags deviations in amounts, velocity and counterparties as activity streams in. Enterprise.

Continuous monitoring and alerts

Risk scores, pattern matches and alerts as data flows, with on-demand scans and alert acknowledgement. Enterprise.

The problem

A single trusted view of core entities, with history and provenance, is hard to keep across systems. A content-addressed, bitemporal store with lineage gives a golden record that carries its own history and sources.

Golden record with history

One trusted view of each core entity that carries its own history and sources, not a snapshot reassembled across systems.

Content-addressed integrity

Every fact keyed by the BLAKE3 hash of its content, so the same fact is never stored twice and integrity can be checked. Community.

Full history on every entity

Valid time and transaction time reconstruct what a master record was, and what the system believed, at any past moment. Community.

Provenance for every field

Lineage traces each record to its sources and forward to what was derived from it. Enterprise.

One source, three languages

Cypher, TQL and SQL read the same master data, so traversals, temporal questions and BI reports agree. Community.

Banking · cross-border fraud rings

Model each transaction as a hyperedge so correspondents and intermediaries stay in the record, and use valid and transaction time to answer what an analyst saw at approval — one query in place of a chase across a warehouse, a graph and a search index.

Pharma · clinical-trial provenance

Store a measurement as one fact with all its participants — patient, site, device, SOP version — so provenance is the record itself rather than a trail kept in sync beside it, and bitemporal queries show which SOP applied when it was taken.

Logistics · parcel event sourcing

Write one hyperedge per parcel event — parcel, hub, driver, previous event — so status is a short traversal instead of a join across systems, with valid time absorbing scanners that replay events out of order.

Capital markets · surveillance

Connect accounts that share traders, securities or strategies through their orders, and let community detection surface tight clusters that fixed rules never encoded — once the modularity threshold and filters are tuned.

AI platforms · agent memory

Keep one hyperedge per memory — agent, user, session, entities — so semantic, keyword and graph recall come from one write, retire stale memories by validity rather than deletion, and record each fact's source and confidence.

Healthcare · patient timelines

Merge records from many systems at insert time by content-addressed identity, and keep valid time apart from each system's transaction time to reconstruct what a chart showed at the moment of a decision.